Dalcetrapib and anacetrapib increase apolipoprotein E-containing HDL in rabbits and humans
Bibliographic record
Abstract
The large HDL particles generated by administration of cholesteryl ester transfer protein inhibitors (CETPi) remain poorly characterized, despite their potential importance in the routing of cholesterol to the liver for excretion, which is the last step of the reverse cholesterol transport. Thus, the effects of the CETPi dalcetrapib and anacetrapib on HDL particle composition were studied in rabbits and humans. The association of rabbit HDL to the LDL receptor (LDLr) in vitro was also evaluated. New Zealand White rabbits receiving atorvastatin were treated with dalcetrapib or anacetrapib. A subset of patients from the dal-PLAQUE-2 study treated with dalcetrapib or placebo were also studied. In rabbits, dalcetrapib and anacetrapib increased HDL-C by more than 58% (P < 0.01) and in turn raised large apo E-containing HDL by 66% (P < 0.001) and 59% (P < 0.01), respectively. Additionally, HDL from CETPi-treated rabbits competed with human LDL for binding to the LDLr on HepG2 cells more than control HDL (P < 0.01). In humans, dalcetrapib increased concentrations of large HDL particles (+69%, P < 0.001) and apo B-depleted plasma apo E (+24%, P < 0.001), leading to the formation of apo E-containing HDL (+47%, P < 0.001) devoid of apo A-I. Overall, in rabbits and humans, CETPi increased large apo E-containing HDL particle concentration, which can interact with hepatic LDLr. The catabolism of these particles may depend on an adequate level of LDLr to contribute to reverse cholesterol transport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".